The trend of change in cervical tumor size and time to death of hospitalized patients in northwestern Ethiopia during 2018–2022: Retrospective study design

Abstract Background and Aims Cervical cancer is the fourth most common cause of cancer‐related death in the world. The objective of this study was to determine factors that affect the longitudinal change of tumor size and the time to death of outpat Methods A retrospective follow‐up study was carried out among 322 randomly selected patients with cervical cancer at the University of Gondar Referral Hospital from May 15, 2018 to May 15, 2022. Data were extracted from the patient's chart from all patients' data records. Kaplan–Meier estimator, log‐rank test, the Cox proportional‐hazard model, and the joint model for the two response variables simultaneously were used. Results Among 322 outpatients with cervical cancer, 148 (46%) of them were human immunodeficiency virus (HIV) positive and 107 (33.3%) of them died. The results of joint and separate models show that there is an association between survival and the longitudinal data in the analysis; it indicates that there is a dependency between longitudinal terms of cervical tumor size and time‐to‐death events. A unit centimeter square rise in tumor size, corresponding to an exp(0.8502) = 2.34 times, significantly raised the mortality risk. Conclusion The study showed that HIV, stage of cancer, treatment, weight, history of abortion, oral contraceptive use, smoking status, and visit time were statistically significant factors for the two outcomes jointly. Implications As a result, adequate health services and adequate resource allocations are critical for cervical cancer control and prevention programs. Therefore, the government should provide adequate funding and well‐trained health professionals to hospitals to sustain screening programs with appropriate coverage of cervical cancer patient treatments.


| INTRODUCTION
Cervical cancer is the fourth most common cancer among women worldwide, with an estimated incidence of 570,000 cases and 311,000 deaths, as reported in 2018. 1 This study aims to assess the epidemiology and risk factors in developing cervical cancer. An estimated 85% of deaths from cervical cancer worldwide occur in middle-and low-income countries, which have mortality rates 18 times higher than that in developed countries. Cervical cancer is known to be caused by oncogenic subtypes of the human papillomavirus (HPV). The risk factors for developing cervical cancer include: sex with multiple partners, sexual activity at an early age, having many children, use of birth control pills, smoking, low socioeconomic status, sexually transmitted diseases, oral contraceptive use, and immune disorders. Epidemiological studies have shown that the risk of developing cervical cancer and contracting genital HPV infection is influenced by a variety of factors. Thus, cervical cancer is due to a variety of additional factors working together with cancer-associated strains of HPV. 1 The cervical cancer epidemic in Africa is profound and complex with noninfectious and infectious risk factors and etiologic components. The cervical cancer epidemic in Africa is considered by the dual burden of noncommunicable and communicable disease, 2 health service preventive delivery challenges, [3][4][5] shortages of human resources for health, 6 access to treatment shortages, and low cervical cancer consciousness among the health providers and population. 7,8 Many research findings show that the HPV vaccine is effective in both preventing genital warts and cervical lesions in those patients who are vaccinated; the result found that patients who are vaccinated for HPV have a lower risk of developing cervical cancer than those patients who are not vaccinated. [9][10][11][12] However, the Centers for Disease Control and Prevention does not recommend HPV vaccinations for people older than 26, individuals aged between 27 and 45, and who are not effectively vaccinated due to a risk for new HPV infection. 13 Screening for cervical cancer is still essential due to the inefficient HPV vaccine protection offered. HPV vaccination protects only 70% of women against cervical cancer. 15 Techniques of screening include: visual inspection with acetic acid (VIA) with HPV testing and Pap smear for risk of HPV types. Some of these methods, such as rapid HPV DNA and VIA testing, are preferred in developing nations due to their ease and cost of manufacture. 16 Due to the lack of comprehensive cervical cancer treatments, early diagnosis and screening could reduce related mortality and morbidity. 17 Artificial intelligence (AI) provides an automated diagnosis that significantly resolves the screening issue, which is verified. 18 In recent years, AI has been used to diagnose a growing number of diseases, particularly in skin malignancies, 19 imaging tumors, 20 classification and detection of retinal diseases, 21 and gynecologic cancer. 22 AI can use sophisticated algorithms for image classification and recognition, process data autonomously, and extract features. [23][24][25][26] In 2020, there were 342,000 cervical cancer deaths; approximately 90% of these deaths occurred in low-and middle-income countries. 26 Programs that enable girls to receive vaccines against HPV infection and women to receive frequent screenings and appropriate care are in place in high-income nations. Screening makes it possible to find precancerous lesions at an early stage when they are still treatable. In low-and middle-income countries, cervical cancer is frequently detected only after it has progressed and symptoms appear, as access to these prophylactic practices is limited.
Additionally, access to cancer treatments (e.g., cancer surgery, radiation, and chemotherapy) can be restricted, which could have an adverse impact on the condition. 27 A total of 120,000 new instances of cervical cancer are diagnosed each year in Africa, accounting for 20% of all new cervical cancer diagnoses worldwide. Women in Africa make up a sizable portion of those who lack access to treatment and care for cervical cancer. The conventional surgical treatment for early cervical cancer, a radical hysterectomy, is not performed in many clinics and in many nations due to a lack of experience. The same is true of several In Ethiopia, cervical cancer screening (CCS) guidelines advocate a "screen-and-treat" method, in which women aged between 30 and 49 years are screened and treated with cryotherapy. The guidelines suggested annual screening for women who were HIV positive and three times annual screening for other women, but the screening was not consistent and was usually determined by the availability of resources. 29 Women and their families are affected by cervical cancer anywhere in the world, but notably in places with few resources for screening, prevention and treatment. Cervical cancer affected 604,237 women worldwide in 2020, accounting for 6.5% of all female cancer cases.
In 36 low-and middle-income nations, mostly in sub-Saharan Africa, cervical cancer affects women more frequently than any other type of cancer. In 2020, cervical cancer is predicted to claim the lives of 341,843 women, 90% of whom live in underdeveloped nations with severely restricted access to services for prevention, screening, and treatment. Today, cervical cancer claims the lives of women more than childbirth does. 30,31 Ethiopia recorded 4884 deaths and 6294 new cases of cervical cancer in 2018, one of the utmost rates in the world. 1 The investigator observed, from the University of Gondar Referral Hospital (UGRH), that the number of patients with cervical cancer being admitted to the UGRH has been rising year over year.
Every year, related to cervical cancer, there are several cases of women who are dead or lost to follow-up. This suggests that there are factors influencing both the survival status of cervical cancer patients discharged from the hospital and the progression of cancers.
This calls for a change in healthcare priorities and the most recent information on the development and associated difficulties of cervical cancer in Ethiopia. Therefore, it is crucial to evaluate the variables that influence the longitudinal evolution of tumor size and the time to event (death) of outpatients with cervical cancer.
Furthermore, it appears from studies that less than 10% of women in Ethiopia had CCS. 10,11,32 The facts about Ethiopia mentioned above indicate that many women are at high risk for cervical cancer. While some studies have been done on cervical cancer, the majority of those done in Ethiopia concern knowledge, screening practices, and factors that predict how long cervical cancer patients will live after diagnosis. These studies range from 2008 to 2012. 10,33 The tumor size of outpatients with cervical cancer, which is one of the key factors affecting prognosis, was not highlighted in the study. In addition to this, they did not show covariates that are

| Study area
The study area was the UGRH, which is located 720 km northwest of Ethiopia's capital city of Addis Ababa. The hospital has 500 beds available. The referral hospital provides for more than seven million populations in the catchment area. It provides subspecialty and specialty services, including internal medicine, surgery, pediatrics, gynecology and obstetrics, ophthalmology, psychiatry, and so on, in its outpatient and inpatient clinics. Based on registered data, there are more than 10,000 delivery services for mothers annually. 34

| Data
The target population of this research was all cervical cancer patients who were hospitalized between May 15, 2018 and May 15, 2022 and who met all the inclusion criteria included in this study. Patients who began cervical cancer therapy at the UGRH from May 15, 2018 to May 15, 2022 and who had at least two follow-up visits to the department clinic for prescription refills were included in this study.
Data regarding the repeated measurement of tumor size and time to death were extracted from the outpatient's chart, which contains clinical information and sociodemographics on all outpatients with cervical cancer who were followed up. In this study, outpatients with cervical cancer represent the number of patients who transfer from the hospital to another hospital or who either leave the hospital by any means or who follow the clinical treatment up to the discharge date or die before completing the treatment for any accident other than cervical cancer or those who are on treatment. The longitudinal submodel of the joint model was described both by the conventional linear mixed-effects model assuming homogeneous within-subject variance and by incorporating subject-specific variance. Longitudinal data sets consisted of an outcome variable, y ij , and a px1 vector covariate, x 1i , observed at times t = 1, 2, 3, …, n i , for subject i = 1, 2, 3, …; the subject-specific variance was used to assess whether individuals with different tumor size variabilities have different influence on time to event (death) of outpatients with cervical cancer to analyze changes over time. 35,36 The timing of an event is examined using a survival model that accounts for the time to occurrence and the censor and/or truncation. The hazard function is widely used to express the risk of an event at time t and obtain from the probability that the individual gets the event at time t, assuming that he or she survive at that time. Kaplan-Meier (KM) estimator is a nonparameter estimator of survival analysis, which is used to describe the survival of patients both graphically and numerically.

| Methods
The joint model consists of two linked submodels, the measurement model for the longitudinal process and the time-to-event model for the survival process. The joint modeling approach was used to obtain less bias and more efficient estimates. The association between longitudinal process and survival process may arise in two ways. One is through the use of common independent factors, and the other is through stochastic dependency between w 1i and w 2i.
Estimation for joint models is based on the maximization of the log-likelihood corresponding to the joint distribution of the time-to-event T and longitudinal outcomes y, where ( ) denotes the full parameter vector, with θ t denoting the parameters for the event time, θ y the parameters for the longitudinal outcome, and θ b the parameters for the random effect.
Assume that given the observed history, the censoring mechanism and the visiting process are independent of the true event times and future longitudinal process.
The joint density for the longitudinal response together with random effect, where qb denotes the dimensionality of random effects' vector and can be achieved using the expectation maximization algorithm or It is required to compare many models using various methods to choose the parsimonious model that best fits the provided data. This paper used Akaike's information criterion, Bayesian information criterion, and likelihood ratio test. 38 The precise nature of the joint model was selected by comparing via standard error, association parameter (α), and confidence interval.
The association parameters quantify the magnitude of the association between the longitudinal process and the event process. It indicates that there is a dependency between longitudinal terms of cervical tumor size and time-to-death events. For a better understanding, we used standardized residual plots, and the hypothesis test was a twosided test. The data were entered and cleaned using SPSS version 20, and they were analyzed using R statistical software version 4.1.3.

| Descriptive analysis
In this study, we used the guidelines for reporting statistics for clinical research in urology 39

| Log-rank test and KM estimates
The KM curve for the smoking status of cervical cancer patients shows that patients who do not smoke have a higher chance of surviving than patients who smoke. The survival probability of outpatients who do not take oral contraceptives is greater than that of outpatients who do, according to the plot of the KM curve for oral contraceptive use. Plots of the KM estimates for the two selected categorical covariates: oral contraceptives and smoking status are displayed in Table 2 and the remaining categorical variables are presented in Figure 1. To assess the significance of differences across various factors, log-rank tests were performed on all categorical variables.
According to the null hypothesis, there is no discernible difference between the rates of survival for various categories of categorical variables. The log-rank tests in Table 2 revealed that, at the 5% level of significance, there is no difference between groups of residence and education in terms of the time to death. Death rates among the study groups differ significantly for other categorical factors. The graphical inspection in Figure 2 shows that there is no pattern with respect to time. The PH assumption appears to be supported by covariates such as weight, comorbidity, smoking, history of abortion, and oral contraceptive use. Since the PH assumption had not been violated, the Cox PH model was employed to assess the time-to-event data.
In In significant relationship between the change in tumor size and mortality risk due to cervical cancer.
The parameter estimates for the individual and joint models are roughly similar but not identical; therefore, we must compare the standard errors of the two models separately and together for relevant predictors. Table 5 demonstrates that the joint model has lower standard errors for all significant predictors when compared to the separate models. The significant predictors in the joint model's survival submodel were statistically significant relationships with the risk of death, just like they were in the separate survival model in Table 5, but the joint model's standard error was much lower. The significant variables in the longitudinal submodel model with a joint model have a smaller standard error than the significant factors in the separate longitudinal model. In terms of low standard errors, the joint model fared generally better for this investigation than a separate model. As shown in Table 5, the estimation of the association coefficient in the survival sub-analysis of the joint model was not equal to 0. This suggests that two outcome variables are correlated and this helps us to make valid inferences and conclusions; the joint model was better to fit the data, and there is a statistically significant and nonignorable difference due to the measurement error of tumor size.
In Table 5, both joint models and separate analyses use a random effect. This demonstrates that the variation of random intercepts was greater than the variance of random slopes, pointing to a more    Finally, the researcher suggested that this work can be expanded in the future by integrating significant variables that were not examined in this particular study.

| LIMITATIONS
The main limitation of the study was that some predictors such as the number of sexual partners, age at first sexual intercourse, and others were not available due to the retrospective nature of the data.

ACKNOWLEDGMENTS
The authors are thankful to the University of Gondar Referral and Teaching Hospital for providing us with sufficient data for our study.

CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT
The authors confirm that the data supporting the findings of this study are not publicly available because the data were used for an another analysis. However, data are available from the corresponding author on reasonable requests. The guarantor has full access to all the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

ETHICS STATEMENT
All

TRANSPARENCY STATEMENT
The lead author, Aragaw Eshetie Aguade, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.